STAIJun 21

Leakage-Aware Benchmarking of LLM Forecasting: Real-Time Nowcasts as the Decision-Time Input for Macro Factor Ranking

arXiv:2606.227199.4Has Code
Predicted impact top 46% in ST · last 90 daysOriginality Synthesis-oriented
AI Analysis

For practitioners in quantitative finance, this work provides a rigorous evaluation framework to isolate genuine LLM forecasting ability from information leakage, though the results suggest only marginal benefits over simpler models.

The paper introduces a leakage-controlled benchmark for LLM forecasting in equity factor ranking, using a retrieval-augmented 7B model with decision-time inputs. The pipeline achieves a median monthly Spearman rank IC of +0.154, but the mean IC is not statistically significant, and a kNN baseline shows comparable performance.

Forecasting benchmarks for retrieval-augmented LLMs routinely confound model capability with information leakage: features labeled with a target's timestamp are often not observable at the system's decision time. We study leakage-controlled equity factor ranking with a retrieval-augmented 7B open-source LLM forecaster. At each month-end from 2023-04 to 2026-03, the forecaster observes only decision-time information: lag-shifted FRED macro variables, recent macro-event summaries, and the Cleveland Fed's archived daily CPI nowcast for unreleased current-month inflation. A macro-analog retrieval module selects historical states, a critic LLM compresses them into one tactical rule, and an actor LLM maps the current state and recent rules into scores for seven U.S. equity style factors. The full pipeline obtains a median monthly Spearman rank IC of +0.154, with positive means across three non-overlapping contiguous 12-month subwindows; the mean IC remains statistically underpowered, with a bootstrap 95% confidence interval that includes zero. Non-LLM baselines under the same decision-time constraint demonstrate that a kNN macro-analog model recovers a comparable median IC, indicating that real-time inflation information and macro-similar retrieval explain much of the median signal. The LLM pipeline retains higher mean IC and a stronger long-short allocation sanity check, suggesting that any marginal benefit is concentrated in the extreme rankings that drive long-short portfolio formation. A descriptive audit of the 36 critic rules and per-month case studies appears in the appendix.

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